We collected a new dataset to benchmark (I)nteractive (V)ideo (O)bject (S)egmentation in the HRI scenario. The dataset contains two types of videos: (1) A human teacher showing different household objects in varying poses for interactive learning. (2) Videos of the same objects used in a kitchen setting while serving and eating food. The objects occur both as static objects and active objects being manipulated. Manipulation was performed by both humans and robots. The aim of this dataset is to facilitate incremental learning and immediate use in a collaborative human-robot environments, such as assistive robot manipulation. Datasets that have a similar setting such as ICUBWorld transformations dataset [1]}, and the Core50 dataset [2]} were proposed. These datasets include different instances within the same category. They benchmark solutions to object recognition in a similar HRI setting but do not provide segmentation annotations unlike our dataset. Other datasets were concerned with the activities of daily living such as the ADL dataset [3]}. The dataset was comprised of ego-centric videos for activities. However, such ADL datasets do not contain the required teaching videos to match the HRI setting we are focusing on. Table REF  summarizes the most relevant datasets suited to the HRI setting.
